{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Tool_Without_Region_Inclusion.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfsTg1f_-Pq4",
        "colab_type": "text"
      },
      "source": [
        "**Tasks Accomplished**\n",
        "\n",
        "1) Integrating my program with Kartikaya's more smoothly\n",
        "\n",
        "    a) Figuring out the use of the synthetic control models\n",
        "    b) Adapting the code to the various changes in variable names, format of data, etc.\n",
        "\n",
        "2) Predictor variables have been used to find the similar counties (this has yielded better results than just lagged-outcome variables).\n",
        "\n",
        "    a) The predictor variables used are population, median age, and population density. \n",
        "    Population and median age are drawn from the Census Bureau. \n",
        "    To get population density, I webscraped a wikipedia article and extracted \n",
        "    the land areas of the counties. I then divided the poplation column by the land area column.\n",
        "\n",
        "    b) This was done because the lagged-outcome-variable model yielded incorrect results. \n",
        "    The prediction not match the pre-intervention period and the post-intervention period \n",
        "    kept showing that masks were driving up cases.\n",
        "         i) This was because the donor pool counties had a much lower population (and presumably \n",
        "         population density), even though the initial number of cases matched my selected county.\n",
        "\n",
        "    c) With a combination of lagged-outcome variables and predictor variables,\n",
        "    the model is working well.\n",
        "\n",
        "**Remaining Tasks**\n",
        "\n",
        "1) Do multiple counties simultaneously.\n",
        "\n",
        "2) Look at the counties of states within the same region instead of limiting ourselves to the counties of any given state\n",
        "\n",
        "**Long-term**\n",
        "1) TEX, LATEX, and Overleaf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAPN3HEZeXWE",
        "colab_type": "text"
      },
      "source": [
        "## Importing Libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ot0eI208_We_",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e193ff4f-a9cd-43ad-bb7d-83bc929f829a"
      },
      "source": [
        "#@title DO NOT RUN THIS CELL UNLESS YOU HAVE BEEN DISCONNECTED FROM A SESSION\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive') \n",
        "#Mounts our drive onto our google colab system"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNnvqjTy4pT5",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "fec671d5-65e7-4050-cd67-4ad4698ea920"
      },
      "source": [
        "import sys\n",
        "!pip uninstall tslib\n",
        "#There are two modules called tslib. The one that !pip install accesses is NOT the one we want\n",
        "#This code will uninstall the irrelevant tslib library so that we can then access the relevant one\n",
        "\n",
        "#sys.path.append('/content/gdrive/Shared drives/dev-SyntheticControl/COVID19-synthetic-control-analysis')\n",
        "\n",
        "#Attempted Fix #1:\n",
        "sys.path.append('/content/gdrive/Shared drives/dev-SyntheticControl/COVID19-synthetic-control-analysis')\n",
        "\n",
        "print(sys.path)\n",
        "#Adds the synthetic control library from the google drive to the google colab system"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Skipping tslib as it is not installed.\u001b[0m\n",
            "['', '/env/python', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynload', '/usr/local/lib/python3.6/dist-packages', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.6/dist-packages/IPython/extensions', '/root/.ipython', '/content/gdrive/Shared drives/dev-SyntheticControl/COVID19-synthetic-control-analysis']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfyONqnKtoh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "f192c0d7-7553-416b-ba06-adeba92fd385"
      },
      "source": [
        "#Synthetic control\n",
        "import tslib\n",
        "\n",
        "import tslib.src\n",
        "from tslib.src import tsUtils\n",
        "from tslib.src.synthcontrol.syntheticControl import RobustSyntheticControl\n",
        "\n",
        "#Matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "import matplotlib.dates as mdates\n",
        "import matplotlib as mpl\n",
        "\n",
        "#Webscraping\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "#Miscellaneous\n",
        "import seaborn as sns\n",
        "import copy\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime as dtm\n",
        "from datetime import timedelta as td"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hifrik7neXWM",
        "colab_type": "text"
      },
      "source": [
        "## Input Variables "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tOyDmfVeXWW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "URL1 ='https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'\n",
        "URL2 = \"https://raw.githubusercontent.com/nytimes/covid-19-data/master/mask-use/mask-use-by-county.csv\"\n",
        "URL3 = \"https://www.census.gov/geographies/reference-files/2017/demo/popest/2017-fips.html\"\n",
        "URL4 = \"https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv\""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43Q53bBEeXWf",
        "colab_type": "text"
      },
      "source": [
        "## Finding Similar Counties "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_j-Tb5VssPEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_raw_data(URL1, ST_NAME):\n",
        "  state = pd.read_csv(URL1, index_col=0)\n",
        "  looking_for_alternative = \"cases\"\n",
        "  state=state[state['state']==target].pivot_table(index='county', columns='date', values= looking_for_alternative).fillna(0)\n",
        "  state_data_of_interest = (pd.read_csv(URL1).\n",
        "                            set_index([\"date\", \"county\", \"state\"]).\n",
        "                            drop(\"fips\", axis = 1).\n",
        "                            xs(ST_NAME, level = \"state\").\n",
        "                            reset_index().\n",
        "                            sort_values(by = [\"date\", \"county\"]).\n",
        "                            set_index([\"date\", \"county\"]))\n",
        "\n",
        "  dates = []\n",
        "  column_names = []\n",
        "  for i in days_prior:\n",
        "    dates.append(dtm.strftime(dtm.strptime(intervention, format) - td(days = i), format))\n",
        "    if i == 1:\n",
        "      column_names.append(\"Scaled magnitude of \"+looking_for_alternative+\" (\"+str(i)+\" day prior)\")\n",
        "    else:\n",
        "      column_names.append(\"Scaled magnitude of \"+looking_for_alternative+\" (\"+str(i)+\" days prior)\")\n",
        "  raw_X = (state[dates].\n",
        "      rename(columns = dict(zip(state[dates].columns, \n",
        "                                column_names))).\n",
        "      replace({0:np.nan}).\n",
        "      dropna())\n",
        "  return raw_X, raw_X.applymap(np.log)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhWuIFvClU4m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_age_database_links(URL4):\n",
        "\n",
        "  URL4 = \"https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-detail.html\" #link for webscraping\n",
        "  a = BeautifulSoup(requests.get(URL4).text, \"html.parser\")\n",
        "\n",
        "  age_html = [i for i in [i for i in  [i for i in [i for i in a.find_all(\"div\") if len(i.get_attribute_list(\"class\"))>0 ]]  if not( i.get_attribute_list(\"class\")[0] in [None, \"\"])] if i.get_attribute_list(\"class\")[0].startswith(\"state\")][0]\n",
        "  age_databases = pd.DataFrame( data = [[i.get_text().strip(), i.get_attribute_list(\"href\")[0] ] for i in age_html.find_all(\"a\")], columns = [\"State\", \"Link\"]).set_index(\"State\")\n",
        "  #webscrapes the URL and gets the links to the csv files that contain the census information for each state\n",
        "\n",
        "  return age_databases"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbxZso4DmHsw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def add_population_to_X(X, age_databases, target):\n",
        "\n",
        "  main_df = pd.DataFrame()\n",
        "  for state in age_databases.index: #iterates through the states\n",
        "    if state == target:\n",
        "      try:\n",
        "        x = pd.read_csv(\"https:\" + age_databases[\"Link\"].loc[state]).drop([\"SUMLEV\", \"STNAME\", \"COUNTY\"], axis = 1)\n",
        "        #reads the csv file for each state\n",
        "\n",
        "        x = x.groupby(\"CTYNAME\").apply(np.mean).drop([\"YEAR\"], axis = 1).reset_index()\n",
        "        #the csv file has information for six years about each county\n",
        "        #this averages together the census data across those six years for each county\n",
        "        #the dataframe is grouped by county and the mean function is applied to it\n",
        "\n",
        "        x[\"STATE\"] = state\n",
        "\n",
        "        land_area = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_counties_in_\"+state.replace(\" \", \"_\"))\n",
        "        #for webscraping the area of each county in the state (allowing me to calculate the population density of each county)\n",
        "        land_area = [i for i in land_area if len(i.columns) > 8][0]\n",
        "        #goes to the correct table on the wikipedia page\n",
        "\n",
        "        division = land_area.columns[0] #this is the county/parish\n",
        "        land_area = land_area.drop([\"Map\"], axis = 1, errors = \"ignore\").set_index(division)\n",
        "        #Most wikipedia pages have a column that shows a map of where the county is; I'm dropping it\n",
        "        #I am ignoring errors, because those will probably come from pages that don't have that column, in which case nothing needs to be done\n",
        "        #the county is set as the index\n",
        "\n",
        "        land_area = land_area[ [ land_area.columns[-1]] ].applymap(lambda area: float(\"\".join((area.split()[0]).split(\",\")))  ).rename(columns = {land_area.columns[-1]:\"Land Area\"}).applymap(round).reset_index()\n",
        "        #fixes the formatting of the numbers in the area column (they are strings with commas ever three digits)\n",
        "        \n",
        "        x = x.merge(land_area, left_on = \"CTYNAME\", right_on = division).drop([division], axis = 1)\n",
        "        #merges the census dataframe and the wikipedia dataframe into a single dataframe about the state\n",
        "\n",
        "        x[\"Population Density\"] = x[\"POPESTIMATE\"] / x[\"Land Area\"]\n",
        "        main_df = pd.concat([main_df, x])\n",
        "        #adds in the dataframe about the state to the overall dataframe about the country\n",
        "        print(state)\n",
        "      except:\n",
        "        print(state + \" (Could not add)\") #If anything went wrong, I'm not sure how to fix it, so I'll just ignore that state\n",
        "  main_df = (main_df.rename(columns = {\"CTYNAME\":\"County\", \"STATE\":\"State\"}).\n",
        "            set_index([\"State\", \"County\"])\n",
        "            [[\"POPESTIMATE\", \"MEDIAN_AGE_TOT\", \"Population Density\"]].\n",
        "            rename(columns = dict(zip ( [\"POPESTIMATE\", \"MEDIAN_AGE_TOT\"],\n",
        "                                        [\"Population\", \"Median Age\"]) ),\n",
        "                    errors = \"ignore\" ).\n",
        "            xs(target))\n",
        "\n",
        "  main_df.index = [i.split(\" County\")[0] for i in main_df.index]\n",
        "  X = X.join(main_df)\n",
        "  return X"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9KEKJDQsc4H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def further_scaling(X):\n",
        "  X[\"Population\"] = X[\"Population\"].apply(np.log)\n",
        "  X[\"Population Density\"] = X[\"Population Density\"].apply(np.log)\n",
        "\n",
        "  for i in X.columns:\n",
        "    X[i] = 100*(X[i] - min(X[i]) ) / (max(X[i]) - min(X[i])) #min-max scaling\n",
        "\n",
        "  X.columns = list(X.columns[:-3])+[i+\" (Scaled)\" for i in X.columns[-3:] ]\n",
        "  return X"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzRbkTx5hUz2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Gets the New York Times data\n",
        "\n",
        "def NYTimesMaskData(URL2, ST_NAME):\n",
        "\n",
        "  nyTimesMaskPolicy = pd.read_csv(URL2, dtype=str).set_index(\"COUNTYFP\")\n",
        "  #************************************************************************************************************************\n",
        "  a = dtm.now()\n",
        "  soup = BeautifulSoup(requests.get(URL3).text, \"html.parser\")\n",
        "  spreadsheets = [i for i in soup.find_all(\"a\") if i.get_attribute_list(\"class\")[0] == \"uscb-text-link\"]\n",
        "  countyFIPS_link = [i for i in spreadsheets if \"County\" in i.get_text()][0].get_attribute_list(\"href\")[0]\n",
        "  countyFIPS = pd.read_excel(\"https:\"+countyFIPS_link, skiprows = 4, dtype=str)\n",
        "  countyFIPS[\"County Code\"] = countyFIPS[\"State Code (FIPS)\"] + countyFIPS[\"County Code (FIPS)\"]\n",
        "  countyFIPS = countyFIPS.rename(columns = {\"Area Name (including legal/statistical area description)\":\"Place\"})[[\"Place\", \"County Code\", \"State Code (FIPS)\", \"County Code (FIPS)\"]]\n",
        "\n",
        "  states = list(pd.read_csv(\"https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv\")[\"State\"])\n",
        "  stateFIPS = countyFIPS[countyFIPS[\"Place\"].isin(states)].set_index(\"Place\").applymap(lambda x: x[:2])\n",
        "  countyFIPS = countyFIPS[countyFIPS[\"Place\"].str.lower().apply(lambda x: x.endswith(\"county\"))]\n",
        "  countyFIPS[\"State\"] = \"\"\n",
        "\n",
        "  for i in countyFIPS.index:\n",
        "    number = countyFIPS.loc[i, \"State Code (FIPS)\"]\n",
        "    countyFIPS.loc[i, \"State\"] = stateFIPS.reset_index().set_index(\"County Code\").loc[number][\"Place\"]\n",
        "\n",
        "  state_county_FIPS = (countyFIPS.query(\"State == '\"+ST_NAME+\"'\").\n",
        "                      reset_index(drop = True).\n",
        "                      applymap(lambda x: x.split(\" County\")[0]))\n",
        "\n",
        "  b = dtm.now()\n",
        "  print(\"Time to run function:\", b-a)\n",
        "  state_county_FIPS\n",
        "  #************************************************************************************************************************\n",
        "\n",
        "  weighted_sum = 0\n",
        "  for i in range(5):\n",
        "    weighted_sum += i*nyTimesMaskPolicy[nyTimesMaskPolicy.columns[i]].apply(float)\n",
        "\n",
        "\n",
        "  relevant_FIPS_codes = list((state_county_FIPS[\"State Code (FIPS)\"] + state_county_FIPS[\"County Code (FIPS)\"]).values)\n",
        "  state_counties_mask_usage = (weighted_sum.loc[relevant_FIPS_codes]\n",
        "                              .reset_index().set_index(\"COUNTYFP\")\n",
        "                              .join(state_county_FIPS.set_index(\"County Code\"))[[\"NEVER\", \"Place\"]]\n",
        "                              .rename(columns = {\"NEVER\":\"Usage Index\", \"PLACE\":\"County\"}))\n",
        "  \n",
        "  return state_counties_mask_usage.sort_values(by = \"Usage Index\").iloc[[0, -1]], state_counties_mask_usage"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hec5IpppTClh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_closest_counties(df, county):\n",
        "  new_df = pd.DataFrame(columns = [\"Euclidean Distance from \"+county])\n",
        "  for value in (df.index):\n",
        "    new_df.loc[value] = (((df.loc[county] - df.loc[value])**2).sum())**0.5\n",
        "  return new_df"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Naexb35Qrvfj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "divisions = 3\n",
        "middle_counterfactual = 2\n",
        "\n",
        "def extract_donor_pool(closeness_scores2, state_counties_mask_usage, counties_in_donor_pool,\n",
        "                       divisions = 2, middle_counterfactual = 2):\n",
        "  initial_county_number = round(len(closeness_scores2)/2)\n",
        "  closeness_scores = (closeness_scores2.\n",
        "                      join(state_counties_mask_usage.reset_index().\n",
        "                          set_index(\"Place\")).\n",
        "                      dropna())\n",
        "\n",
        "\n",
        "  mean_score = 3.1 #np.mean(closeness_scores[\"Usage Index\"])\n",
        "  standard_deviation = np.std(closeness_scores[\"Usage Index\"])\n",
        "\n",
        "  if divisions == 3:\n",
        "    closeness_scores[\"Group\"] = closeness_scores[\"Usage Index\"] - mean_score\n",
        "    for i in closeness_scores.index:\n",
        "      if abs(closeness_scores.loc[i][\"Group\"])<=standard_deviation:\n",
        "        closeness_scores.loc[i, \"Group\"] = 1\n",
        "      else:\n",
        "        closeness_scores.loc[i, \"Group\"] = 2*int(closeness_scores.loc[i][\"Usage Index\"] > mean_score)\n",
        "  else:\n",
        "    closeness_scores[\"Group\"] = 2*((closeness_scores[\"Usage Index\"] >= mean_score).apply(int)) \n",
        "  closeness_scores\n",
        "\n",
        "  county_of_interest_group = closeness_scores.loc[county_of_interest][\"Group\"]\n",
        "  counterfactual_group = 2*(county_of_interest_group < 1)\n",
        "  if county_of_interest_group == 1:\n",
        "    counterfactual_group = middle_counterfactual\n",
        "\n",
        "  donor_pool_table = (closeness_scores.\n",
        "                      query(\"Group == \"+str(counterfactual_group)).\n",
        "                      sort_values(by = closeness_scores.columns[0]).\n",
        "                      head(counties_in_donor_pool))\n",
        "  donor_pool = list(donor_pool_table.index)\n",
        "  return donor_pool, closeness_scores, mean_score, standard_deviation"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw9F2c-vQvin",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ce2dc56e-a507-42e2-dc25-4a18ff3fdfdd"
      },
      "source": [
        "format = \"%Y-%m-%d\"\n",
        "county = \"Collin\" #to change\n",
        "target = \"Texas\" #to change\n",
        "\n",
        "specific_intervention_dates = {\"Kansas\":\"2020-06-02\",\n",
        "                               \"Texas\":\"2020-07-02\",\n",
        "                               \"South Carolina\":\"2020-07-29\",\n",
        "                               \"Pennsylvania\":\"2020-07-01\"}\n",
        "\n",
        "date   = \"2020-06-01\" #to change\n",
        "#date = specific_intervention_dates[target]\n",
        "start  = \"2020-03-10\" #to change\n",
        "end    = \"2020-09-15\" #to change\n",
        "max_euclidean_distance = 50\n",
        "counties_in_donor_pool = 6\n",
        "download_output = False\n",
        "\n",
        "\n",
        "ST_NAME = target\n",
        "intervention = date\n",
        "county_of_interest = str(county)\n",
        "days_prior = [1, 7, 15] #How many days before the intervention date do we want to consider?\n",
        "raw_X, X = get_raw_data(URL1, ST_NAME)\n",
        "age_databases = get_age_database_links(URL4)\n",
        "X = add_population_to_X(X, age_databases, target)\n",
        "X = further_scaling(X)\n",
        "extremes, state_counties_mask_usage = NYTimesMaskData(URL2, ST_NAME)\n",
        "\n",
        "closeness_scores2 = (find_closest_counties(X, county_of_interest).\n",
        "                    sort_values(\"Euclidean Distance from \"+county_of_interest).\n",
        "                    query(\"`Euclidean Distance from \"+county_of_interest+\"` <= \"+str(max_euclidean_distance)))\n",
        "\n",
        "donor_pool, closeness_scores, mean_score, standard_deviation = extract_donor_pool(closeness_scores2, state_counties_mask_usage, counties_in_donor_pool,\n",
        "                                                                                  divisions = 2, middle_counterfactual = 0)\n",
        "\n",
        "print(\"Donor pool: \", donor_pool)\n",
        "closeness_scores#[closeness_scores[\"Group\"]==0]"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Texas\n",
            "Time to run function: 0:00:10.478405\n",
            "Donor pool:  ['Randall', 'Potter', 'Smith']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Euclidean Distance from Collin</th>\n",
              "      <th>COUNTYFP</th>\n",
              "      <th>Usage Index</th>\n",
              "      <th>Group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Collin</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>48085</td>\n",
              "      <td>3.553</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denton</th>\n",
              "      <td>6.342480</td>\n",
              "      <td>48121</td>\n",
              "      <td>3.565</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fort Bend</th>\n",
              "      <td>8.686603</td>\n",
              "      <td>48157</td>\n",
              "      <td>3.585</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Montgomery</th>\n",
              "      <td>12.559137</td>\n",
              "      <td>48339</td>\n",
              "      <td>3.390</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Galveston</th>\n",
              "      <td>15.795280</td>\n",
              "      <td>48167</td>\n",
              "      <td>3.336</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Williamson</th>\n",
              "      <td>19.130826</td>\n",
              "      <td>48491</td>\n",
              "      <td>3.526</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>El Paso</th>\n",
              "      <td>19.155847</td>\n",
              "      <td>48141</td>\n",
              "      <td>3.806</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bexar</th>\n",
              "      <td>19.309539</td>\n",
              "      <td>48029</td>\n",
              "      <td>3.688</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travis</th>\n",
              "      <td>19.460573</td>\n",
              "      <td>48453</td>\n",
              "      <td>3.710</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazoria</th>\n",
              "      <td>22.404562</td>\n",
              "      <td>48039</td>\n",
              "      <td>3.466</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cameron</th>\n",
              "      <td>22.898790</td>\n",
              "      <td>48061</td>\n",
              "      <td>3.549</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jefferson</th>\n",
              "      <td>27.346260</td>\n",
              "      <td>48245</td>\n",
              "      <td>3.240</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lubbock</th>\n",
              "      <td>28.384516</td>\n",
              "      <td>48303</td>\n",
              "      <td>3.320</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hidalgo</th>\n",
              "      <td>29.578002</td>\n",
              "      <td>48215</td>\n",
              "      <td>3.625</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tarrant</th>\n",
              "      <td>30.929159</td>\n",
              "      <td>48439</td>\n",
              "      <td>3.508</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nueces</th>\n",
              "      <td>32.331337</td>\n",
              "      <td>48355</td>\n",
              "      <td>3.667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Randall</th>\n",
              "      <td>35.790838</td>\n",
              "      <td>48381</td>\n",
              "      <td>3.067</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bell</th>\n",
              "      <td>36.672060</td>\n",
              "      <td>48027</td>\n",
              "      <td>3.494</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potter</th>\n",
              "      <td>38.061344</td>\n",
              "      <td>48375</td>\n",
              "      <td>3.045</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hays</th>\n",
              "      <td>40.050024</td>\n",
              "      <td>48209</td>\n",
              "      <td>3.816</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ellis</th>\n",
              "      <td>40.362820</td>\n",
              "      <td>48139</td>\n",
              "      <td>3.563</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smith</th>\n",
              "      <td>41.384752</td>\n",
              "      <td>48423</td>\n",
              "      <td>2.919</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gregg</th>\n",
              "      <td>41.725619</td>\n",
              "      <td>48183</td>\n",
              "      <td>3.262</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taylor</th>\n",
              "      <td>41.880840</td>\n",
              "      <td>48441</td>\n",
              "      <td>3.172</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dallas</th>\n",
              "      <td>42.808622</td>\n",
              "      <td>48113</td>\n",
              "      <td>3.588</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazos</th>\n",
              "      <td>44.931528</td>\n",
              "      <td>48041</td>\n",
              "      <td>3.361</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Walker</th>\n",
              "      <td>45.260752</td>\n",
              "      <td>48471</td>\n",
              "      <td>3.373</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Johnson</th>\n",
              "      <td>46.176905</td>\n",
              "      <td>48251</td>\n",
              "      <td>3.356</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Webb</th>\n",
              "      <td>46.195805</td>\n",
              "      <td>48479</td>\n",
              "      <td>3.604</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grayson</th>\n",
              "      <td>46.959971</td>\n",
              "      <td>48181</td>\n",
              "      <td>3.114</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rockwall</th>\n",
              "      <td>47.462215</td>\n",
              "      <td>48397</td>\n",
              "      <td>3.363</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Harris</th>\n",
              "      <td>48.179020</td>\n",
              "      <td>48201</td>\n",
              "      <td>3.562</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kaufman</th>\n",
              "      <td>49.081936</td>\n",
              "      <td>48257</td>\n",
              "      <td>3.333</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Euclidean Distance from Collin COUNTYFP  Usage Index  Group\n",
              "Collin                            0.000000    48085        3.553      2\n",
              "Denton                            6.342480    48121        3.565      2\n",
              "Fort Bend                         8.686603    48157        3.585      2\n",
              "Montgomery                       12.559137    48339        3.390      2\n",
              "Galveston                        15.795280    48167        3.336      2\n",
              "Williamson                       19.130826    48491        3.526      2\n",
              "El Paso                          19.155847    48141        3.806      2\n",
              "Bexar                            19.309539    48029        3.688      2\n",
              "Travis                           19.460573    48453        3.710      2\n",
              "Brazoria                         22.404562    48039        3.466      2\n",
              "Cameron                          22.898790    48061        3.549      2\n",
              "Jefferson                        27.346260    48245        3.240      2\n",
              "Lubbock                          28.384516    48303        3.320      2\n",
              "Hidalgo                          29.578002    48215        3.625      2\n",
              "Tarrant                          30.929159    48439        3.508      2\n",
              "Nueces                           32.331337    48355        3.667      2\n",
              "Randall                          35.790838    48381        3.067      0\n",
              "Bell                             36.672060    48027        3.494      2\n",
              "Potter                           38.061344    48375        3.045      0\n",
              "Hays                             40.050024    48209        3.816      2\n",
              "Ellis                            40.362820    48139        3.563      2\n",
              "Smith                            41.384752    48423        2.919      0\n",
              "Gregg                            41.725619    48183        3.262      2\n",
              "Taylor                           41.880840    48441        3.172      2\n",
              "Dallas                           42.808622    48113        3.588      2\n",
              "Brazos                           44.931528    48041        3.361      2\n",
              "Walker                           45.260752    48471        3.373      2\n",
              "Johnson                          46.176905    48251        3.356      2\n",
              "Webb                             46.195805    48479        3.604      2\n",
              "Grayson                          46.959971    48181        3.114      2\n",
              "Rockwall                         47.462215    48397        3.363      2\n",
              "Harris                           48.179020    48201        3.562      2\n",
              "Kaufman                          49.081936    48257        3.333      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvCQ6zlpeXWz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9a221e47-8893-4c2a-dc87-6ce434c0280c"
      },
      "source": [
        "state = pd.read_csv(URL1, index_col=0)\n",
        "looking_for_alternative = \"cases\"\n",
        "state=state[state['state']==target].pivot_table(index='county', columns='date', values= looking_for_alternative).fillna(0)\n",
        "data = state[[i for i in state.columns if \n",
        "              (dtm.strptime(i, format)<=dtm.strptime(end, format)) and \n",
        "              (dtm.strptime(i, format)>=dtm.strptime(start, format))]].reindex(donor_pool+[county_of_interest])\n",
        "\n",
        "group_of_county = closeness_scores.loc[county_of_interest][\"Group\"]\n",
        "usage_of_county = closeness_scores.loc[county_of_interest][\"Usage Index\"]\n",
        "data[[\"2020-05-30\", \"2020-05-31\", \"2020-06-01\", \"2020-06-02\"]]"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>date</th>\n",
              "      <th>2020-05-30</th>\n",
              "      <th>2020-05-31</th>\n",
              "      <th>2020-06-01</th>\n",
              "      <th>2020-06-02</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>county</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Randall</th>\n",
              "      <td>674.0</td>\n",
              "      <td>674.0</td>\n",
              "      <td>688.0</td>\n",
              "      <td>711.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potter</th>\n",
              "      <td>2321.0</td>\n",
              "      <td>2321.0</td>\n",
              "      <td>2354.0</td>\n",
              "      <td>2432.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smith</th>\n",
              "      <td>204.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collin</th>\n",
              "      <td>1297.0</td>\n",
              "      <td>1312.0</td>\n",
              "      <td>1315.0</td>\n",
              "      <td>1315.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "date     2020-05-30  2020-05-31  2020-06-01  2020-06-02\n",
              "county                                                 \n",
              "Randall       674.0       674.0       688.0       711.0\n",
              "Potter       2321.0      2321.0      2354.0      2432.0\n",
              "Smith         204.0       204.0       210.0       212.0\n",
              "Collin       1297.0      1312.0      1315.0      1315.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iTuZpfIeXW1",
        "colab_type": "text"
      },
      "source": [
        "## Performing Synthetic Control"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB8NzVJGeXW3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "d7c3624e-534d-4ae7-fe05-517871c55c7f"
      },
      "source": [
        "allColumns = data.columns.values\n",
        "years = list(data.columns)\n",
        "caStateKey = county\n",
        "otherStates = [i for i in data.index if i!=county_of_interest]\n",
        "p = 1.0\n",
        "yearTrainEnd = intervention\n",
        "#*******************************************************************************\n",
        "testYears=list(years)[list(years).index(date):len(list(years))]\n",
        "trainingYears=list(years)[list(years).index(start):list(years).index(date)]\n",
        "trainDF = data[trainingYears].T.reset_index(drop = True)\n",
        "testDF = data[testYears].T.reset_index(drop = True)\n",
        "trainDF = data[trainingYears].T.reset_index(drop = True)\n",
        "testDF = data[testYears].T.reset_index(drop = True)\n",
        "\n",
        "\n",
        "\n",
        "singvals = min(4, len(donor_pool))#counties_in_donor_pool\n",
        "print(singvals)\n",
        "print(len(donor_pool))\n",
        "rscModel = RobustSyntheticControl(caStateKey, singvals, len(trainDF), probObservation=1.0, modelType='svd', svdMethod='numpy', otherSeriesKeysArray=otherStates)\n",
        "rscModel.fit(trainDF)\n",
        "denoisedDF = rscModel.model.denoisedDF()\n",
        "predictions = []\n",
        "predictions = np.dot(testDF[otherStates], rscModel.model.weights)\n",
        "actual = data.loc[caStateKey]\n",
        "model_fit = np.dot(trainDF[otherStates][:], rscModel.model.weights)\n",
        "\n",
        "\n",
        "trainDF"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>county</th>\n",
              "      <th>Randall</th>\n",
              "      <th>Potter</th>\n",
              "      <th>Smith</th>\n",
              "      <th>Collin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>655.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>658.0</td>\n",
              "      <td>2276.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>1236.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>671.0</td>\n",
              "      <td>2317.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1278.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>674.0</td>\n",
              "      <td>2321.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1297.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>674.0</td>\n",
              "      <td>2321.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1312.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>83 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "county  Randall  Potter  Smith  Collin\n",
              "0           0.0     0.0    0.0     3.0\n",
              "1           0.0     0.0    0.0     3.0\n",
              "2           0.0     0.0    0.0     4.0\n",
              "3           0.0     0.0    3.0     5.0\n",
              "4           0.0     0.0    3.0     7.0\n",
              "..          ...     ...    ...     ...\n",
              "78        655.0  2266.0  200.0  1217.0\n",
              "79        658.0  2276.0  202.0  1236.0\n",
              "80        671.0  2317.0  204.0  1278.0\n",
              "81        674.0  2321.0  204.0  1297.0\n",
              "82        674.0  2321.0  204.0  1312.0\n",
              "\n",
              "[83 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgYLhVcDeXXA",
        "colab_type": "text"
      },
      "source": [
        "## Plotting graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4SU9VeDeXXA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "8e2ade0c-efd1-4224-c41b-cdd75446bee4"
      },
      "source": [
        "face_state_dict = {\"Texas\":\"yellow\",\n",
        "                   \"South Carolina\":\"sandybrown\",\n",
        "                   \"Kansas\":\"gold\",\n",
        "                   \"West Virginia\":\"papayawhip\",\n",
        "                   \"Ohio\":\"lightblue\",\n",
        "                   \"Pennsylvania\":\"aquamarine\"}\n",
        "\n",
        "try:\n",
        "  fig = plt.figure(figsize=(40,10), facecolor = face_state_dict[ST_NAME])\n",
        "except:\n",
        "  fig = plt.figure(figsize=(40,10))\n",
        "tick_spacing = 1\n",
        "intDate=yearTrainEnd\n",
        "plt.plot(years, actual)\n",
        "\n",
        "plt.xlabel('Time Period', family = \"serif\", \n",
        "           fontsize = 25, style = \"italic\")\n",
        "plt.ylabel('Number of confirmed Cases', family = \"serif\", \n",
        "           fontsize = 25, style = \"italic\")\n",
        "plt.plot(trainingYears, model_fit, label='fitted model')\n",
        "plt.plot(testYears, predictions, label='counterfactual')\n",
        "\n",
        "\n",
        "plt.title(\"Counterfactual Consists of the \"+str(singvals)+\" Most Suitable Counties from the \"+str(len(donor_pool))+\" Counties in the Donor Pool\",\n",
        "          fontsize = 30,\n",
        "          fontname = \"Serif\")\n",
        "\n",
        "donor_pool = donor_pool[: min(6, len(donor_pool))]\n",
        "\n",
        "plt.suptitle(county_of_interest+\", \"+ST_NAME+\" (Donor Pool: \"+\", \".join(donor_pool[:-1])+\", and \"+donor_pool[-1]+\")\",\n",
        "             fontname = \"Serif\",\n",
        "             fontsize = 35,\n",
        "             weight = 800)\n",
        "\n",
        "plt.legend(['Actual County: '+county_of_interest+\" (Mask-wearing index of \"+str(usage_of_county)+\")\",\n",
        "            \"Fitted Model\",\n",
        "            \"Counterfactual\"],\n",
        "           prop = {\"size\":20,\n",
        "                   \"family\":\"Serif\"},\n",
        "           bbox_to_anchor = (0.3, 1),\n",
        "           facecolor = \"lightgray\")\n",
        "n = 7  # Keeps every 7th label\n",
        "ax = plt.gca()\n",
        "[l.set_visible(False) for (i,l) in enumerate(ax.xaxis.get_ticklabels()) if i % n != 0]\n",
        "\n",
        "#plt.xticks(rotation=90)\n",
        "plt.axvline(x=str(intDate), color='r', linestyle='--', linewidth=4)\n",
        "plt.grid(axis = \"y\")\n",
        "\n",
        "if download_output == True:\n",
        "  file_names = [caStateKey+\" (\"+str(singvals)+' Best Values from a Donor pool of '+str(len(donor_pool))+' Values Used) '+dtm.strftime(dtm.now(), \"%m_%d_%Y_%H%M%S\")+\".png\",\n",
        "                \"Closeness Scores of Nearby Counties to \"+caStateKey+\" \"+dtm.strftime(dtm.now(), \"%m_%d_%Y_%H%M%S\")+\".csv\",\n",
        "                \"Input Variables For Determining Closenessto \"+caStateKey+\" \"+dtm.strftime(dtm.now(), \"%m_%d_%Y_%H%M%S\")+\".csv\",\n",
        "                \"Data for the Donor Pool (and for \"+caStateKey+\") \"+dtm.strftime(dtm.now(), \"%m_%d_%Y_%H%M%S\")+\".csv\"]\n",
        "  plt.savefig(file_names[0], facecolor = face_state_dict[ST_NAME]) #If so, then save the graph\n",
        "  closeness_scores.to_csv(file_names[1])\n",
        "  X.to_csv(file_names[2])\n",
        "  data.to_csv(file_names[3])\n",
        "  from google.colab import files\n",
        "  for i in file_names:\n",
        "    files.download(i)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QADdQ8G9eXXG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "62086caf-1640-4116-cfdd-4d32473f1968"
      },
      "source": [
        "change = round(100*(predictions[-1] / data.loc[county_of_interest].iloc[-1])-100, \n",
        "      2)\n",
        "\n",
        "if change < 0:\n",
        "  word = \"fewer\"\n",
        "else:\n",
        "  word = \"more\"\n",
        "\n",
        "if  closeness_scores[\"Group\"].loc[county_of_interest] == 2:\n",
        "  phrase = \"a more lenient\"\n",
        "else:\n",
        "  phrase = \"a stricter\"\n",
        "\n",
        "\n",
        "print((\"As of {}, {} County would have had {}% \"+\n",
        "       word+\" cases if it had followed \"+phrase+\" policy toward Covid-19.\").\n",
        "      format(\n",
        "          dtm.strftime(dtm.strptime(data.columns[-1], format), \"%B %d, %Y\"),\n",
        "          county_of_interest,\n",
        "          abs(change)\n",
        "          ))"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "As of September 15, 2020, Atchison County would have had 22.8% fewer cases if it had followed a stricter policy toward Covid-19.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDuaBHfOcrRd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "956c5245-1522-4073-8a1d-1d4a226caa47"
      },
      "source": [
        "closeness_scores.reindex(donor_pool)[\"Usage Index\"].mean()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.235"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    }
  ]
}